Weather Events and their Impacts on Human Health and Economics

Synopsis
Load up all dependencies and set global variables
Download Data if Missing
Load Data onto R
The Data
Data Processing
Results

Synopsis

The NOAA Storm Database receives Storm Data from the National Weather Service from across the US. This project aims to quantify the impact of various documented storms from an Economic as well as Human perspective. The idea is to compare and contrast various events to answer the following questions:

Across the United States, which types of events are most harmful with respect to population health?
Across the United States, which types of events have the greatest economic consequences?

This work was done as a part of a project towards the completion of the Reproducible Research course in the Data Science Specialization. This knitr generated publication documents all the work done (in R) towards the completion of said project.

Read the full data documentation

Load up all dependencies and set global variables

library(dplyr)
library(ggplot2)
library(knitr)
library(xtable)
library(magrittr)
library(data.table)

knitr::opts_chunk$set(
    fig.align = "center",
    fig.height = 6,
    fig.path = "figs/fig-",
    fig.width = 6,
    message = FALSE,
    warning = FALSE,
    comment = NA
)

Download Data if Missing

destfile <- "repdata_data_StormData.csv.bz2"
fileURL <- "https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"

if(!file.exists(destfile)){
  res <- tryCatch(download.file(fileURL,
                            destfile=destfile,
                            method="auto"),
              error=function(e) 1)
}

Load Data onto R

df <- as.data.table(read.csv("repdata_data_StormData.csv.bz2"))

The Data

Table

Columns

	colnames(df)
1	STATE__
2	BGN_DATE
3	BGN_TIME
4	TIME_ZONE
5	COUNTY
6	COUNTYNAME
7	STATE
8	EVTYPE
9	BGN_RANGE
10	BGN_AZI
11	BGN_LOCATI
12	END_DATE
13	END_TIME
14	COUNTY_END
15	COUNTYENDN
16	END_RANGE
17	END_AZI
18	END_LOCATI
19	LENGTH
20	WIDTH
21	F
22	MAG
23	FATALITIES
24	INJURIES
25	PROPDMG
26	PROPDMGEXP
27	CROPDMG
28	CROPDMGEXP
29	WFO
30	STATEOFFIC
31	ZONENAMES
32	LATITUDE
33	LONGITUDE
34	LATITUDE_E
35	LONGITUDE_
36	REMARKS
37	REFNUM

Data Processing

Data Subsetting

Since we’re only interested in:

Event Type EVTYPE
Fatalities FATALITIES
Injuries INJURIES
Damange to Property PROPDMGEPROPDMGEXP
Damange to Crops CROPDMGECROPDMGEXP

dmg <- df[,c("EVTYPE", "FATALITIES", "INJURIES", "PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP")]

Quantifying Data

Making the Data Visualization-ready

The dataset used some interesting notation to represent the amount of damage done to their crops and property. For example,

is represented in two columns as,

Let’s have a look at the "xEXP" values present in the dataset.

unique(dmg$PROPDMGEXP)

 [1] K M   B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
Levels:  - ? + 0 1 2 3 4 5 6 7 8 B h H K m M

unique(dmg$CROPDMGEXP)

[1]   M K m B ? 0 k 2
Levels:  ? 0 2 B k K m M

Corresponding to each unique symbol, we need to assign a numeric value. Let’s create key-value pairs with our assigned exponents as values to facilitate our transition from this current state (shown here) to this.

1. Change all `xEXP` entries to uppercase.

cols <- c("PROPDMGEXP", "CROPDMGEXP")
dmg %<>% mutate_at(cols, toupper)
dmg <- as.data.table(dmg)

2. Map property and crop damage alphanumeric exponents to numeric values.

propDmgKey <-  c("\"\"" = 10^0,
                 "-" = 10^0, 
                 "+" = 10^0,
                 "0" = 10^0,
                 "1" = 10^1,
                 "2" = 10^2,
                 "3" = 10^3,
                 "4" = 10^4,
                 "5" = 10^5,
                 "6" = 10^6,
                 "7" = 10^7,
                 "8" = 10^8,
                 "9" = 10^9,
                 "H" = 10^2,
                 "K" = 10^3,
                 "M" = 10^6,
                 "B" = 10^9)

cropDmgKey <-  c("\"\"" = 10^0,
                "?" = 10^0, 
                "0" = 10^0,
                "K" = 10^3,
                "M" = 10^6,
                "B" = 10^9)

3. Replace the values in `PROPDMGEXP` with their corresponding numeric values.

dmg[, PROPDMGEXP := propDmgKey[as.character(dmg[,PROPDMGEXP])]]
dmg[is.na(PROPDMGEXP), PROPDMGEXP := 10^0 ]

dmg[, CROPDMGEXP := cropDmgKey[as.character(dmg[,CROPDMGEXP])] ]
dmg[is.na(CROPDMGEXP), CROPDMGEXP := 10^0 ]

NOTE: Using mutate for this task doesn’t work as intended for some reason

4. Use `mutate` to create columns for `PropertyDamage`, `CropDamage` and `TotalDamage` and get rid of the raw column data

dmg %<>% 
  mutate(PropertyDamage = PROPDMG*PROPDMGEXP, CropDamage = CROPDMG*CROPDMGEXP) %>% 
  mutate(TotalDamage = PropertyDamage+CropDamage) %>% 
  select(-c("PROPDMG", "PROPDMGEXP", "CROPDMG", "CROPDMGEXP"))

5. Preview the Dataframe before proceeding

dmg[0:100,]

Results

Summary of Data

Let’s first summarize the data to find the total Economic and Manpower Damage of these Weather events

dmg %>% 
  group_by(EVTYPE)  %>% 
  summarize(PropertyDamage = sum(PropertyDamage),
            CropDamage = sum(CropDamage),
            TotalDamage = sum(TotalDamage),
            Injuries = sum(INJURIES),
            Fatalities = sum(FATALITIES))

Weather Events and their Impacts on Human Health and Economics